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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (1): 11-29    DOI: 10.1631/jzus.C12a0200
    
Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain
Ozoemena Anthony Ani, He Xu, Yi-ping Shen, Shao-gang Liu, Kai Xue
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China
Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain
Ozoemena Anthony Ani, He Xu, Yi-ping Shen, Shao-gang Liu, Kai Xue
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China
 全文: PDF 
摘要： Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot (AWMR) in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design, evaluation, optimization, and motion control of AWMRs. In this paper, decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction. Numerical analysis of traction performance in terms of drawbar pull, tractive efficiency, and driving torque is carried out for wheels of different radii, widths, and lug heights, under different wheel slips. Effects of normal forces on wheels are analyzed. Results presented in figures are discussed and used to draw some conclusions. Furthermore, a multiobjective optimization (MOO) method for achieving optimal mobility is presented. The MOO problem is formulated based on five independent variables including wheel radius r, width b, lug height h, wheel slip s, and wheel rotation angle θ with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio. Genetic algorithm in MATLAB is used to obtain optimized wheel design and traction control parameters such as drawbar pull, tractive efficiency, and dynamic traction ratio required for good mobility performance. Comparison of MOO results with experimental results shows a good agreement. A method to apply the MOO results for online traction and mobility prediction and control is discussed.
关键词： Autonomous wheeled mobile robot (AWMR);  Terramechanics;  Traction;  Motion control;  Multiobjective optimization (MOO);  Genetic algorithm (GA)    
Abstract: Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot (AWMR) in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design, evaluation, optimization, and motion control of AWMRs. In this paper, decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction. Numerical analysis of traction performance in terms of drawbar pull, tractive efficiency, and driving torque is carried out for wheels of different radii, widths, and lug heights, under different wheel slips. Effects of normal forces on wheels are analyzed. Results presented in figures are discussed and used to draw some conclusions. Furthermore, a multiobjective optimization (MOO) method for achieving optimal mobility is presented. The MOO problem is formulated based on five independent variables including wheel radius r, width b, lug height h, wheel slip s, and wheel rotation angle θ with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio. Genetic algorithm in MATLAB is used to obtain optimized wheel design and traction control parameters such as drawbar pull, tractive efficiency, and dynamic traction ratio required for good mobility performance. Comparison of MOO results with experimental results shows a good agreement. A method to apply the MOO results for online traction and mobility prediction and control is discussed.
Key words: Autonomous wheeled mobile robot (AWMR)    Terramechanics    Traction    Motion control    Multiobjective optimization (MOO)    Genetic algorithm (GA)
收稿日期: 2012-08-06 出版日期: 2013-01-03
CLC:  TP24  
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Ozoemena Anthony Ani
He Xu
Yi-ping Shen
Shao-gang Liu
Kai Xue

引用本文:

Ozoemena Anthony Ani, He Xu, Yi-ping Shen, Shao-gang Liu, Kai Xue. Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain. Front. Inform. Technol. Electron. Eng., 2013, 14(1): 11-29.

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